Video Segmentation and Object Tracking using Improvised Deep Learning Algorithms

Video segmentation and object tracking are critical tasks in computer vision, with applications ranging from autonomous driving to surveillance and video analytics. Traditional approaches often struggle with challenges like occlusion, background clutter, and high computational costs, limiting their accuracy and efficiency in realworld scenarios. This research addresses these issues by employing improvised deep learning algorithms, specifically Convolutional Neural Networks (CNN), VGG, and AlexNet, to enhance the precision and speed of video segmentation and object tracking. The proposed method integrates feature extraction capabilities of CNN with the deeper architecture of VGG for improved feature representation and AlexNet's computational efficiency to ensure scalability. A novel multistage training process is implemented, where CNN provides initial object localization, VGG refines segmentation boundaries, and AlexNet accelerates tracking in real-time. The framework was trained and evaluated on benchmark datasets such as DAVIS and MOT17, covering diverse scenarios with varying complexities. The results show significant improvements in accuracy and speed compared to existing methods. On the DAVIS dataset, the approach achieved a segmentation accuracy of 89.7% and an Intersection over Union (IoU) score of 86.5%. For object tracking on MOT17, the system attained a MultiObject Tracking Accuracy (MOTA) of 82.3% and an average frame processing rate of 35 frames per second (FPS), outperforming baseline methods by 8.5% in accuracy and 15% in computational efficiency. The CNN, VGG, and AlexNet in a unified framework offers a robust solution for video segmentation and object tracking, demonstrating enhanced accuracy, adaptability, and real-time performance. These findings hold promise for applications in areas requiring reliable and efficient visual analysis.